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  • Database creation with SQL , cleansing and collation of the data , querying the tables with active joins, carrying out derived calculations, summarizing the data for complex views and making the Data Science applications Business ready
  • Powerful summarization, formatting, reporting and graphical representation capabilities with Excel to convert data to insights for better decision making.
  • Visual Analytics with Tableau comprising of Drag and drop Calculations, Instant trend lines, reference lines, Interactive Table calculation and editing, Geographical search and Lasso selection
  • Extensive range of Univariate & multivariate statistical techniques with business applications of machine learning algorithms with R
  • Python frameworks and methods that can be used for routine operations of descriptive and Inferential statistics along with powerful libraries LikePandas, Matpotlib & Numpy
  • Experience sharing by industry experts form different business sectors 
  • Important skills for Fresher’s and working professionals to enhance their careers
  • Membership to KJA–KAALP Job Assistance program

200 hours

E-learning

Practice Datasets

& Case Studies

40+

Quizzes

Learn Anywhere

Anytime

Industry

Relevant

Webinars by

Industry Experts

Certification

by Kaalp

200 hours Elearning

Practice Datasets & Case Studies

40+ Quizzes

Learn Anywhere Anytime

Industry Relevant

Webinars by Industry Experts

Certification by Kaalp

The course covers 5 modules viz SQL, Excel, Tableau R & Python and will equip learners with skills in data management, reporting, visualization, advanced analytics and machine learning with R & Python.
This SQL course provides a solid foundation of the SQL language that covers the concepts of SQL programming including database objects, storing, retrieving and manipulating data using various techniques. Learning SQL will allow you to mine data with greater efficiency. The training on Excel will make you more proficient with powerful reporting& analysis. While business reporting is critical for all organizations for good decision making analyzing data with basic statistical functions using Excel is an added advantage of this curriculum.
Tableau with its range of business intelligence applications is known for its flexible intuitive & interactive visualization for effective business insights. Tableau software has real-time data analytics capabilities and supports cloud as well. The benefits of using Tableau software are its flexibility to explore data with its advanced visualization feature, drill up or down across multiple dimensions of business that could provide effective measures so that monitoring & controlling could be done after carefully studying the trends & patterns.
R is the most popular programming languages used for data analysis and statistical modelling.This training course provides an in dept understanding of data analytics concepts as well as application of R language in data manipulation, various calculation and graphical display. In addition this content covers statistical functions and techniques and machine learning which will enable you to learn how to user R features such as random forest and decision trees for applications to learn from data and arrive at decisions with minimal human intervention.
Python is a general-purpose programming language which can be used for a wide spectrum of applications such as business data analysis, artificial intelligence and scientific computing. Python is built on OOPS (Object Oriented Programming) and caters to a wide spectrum of analytical needs ranging from data access, data wrangling, munging, dashboards, visualizations & a comprehensive exposure to state of the art Machine Learning and Artificial Intelligence techniques.
Students from any discipline who are looking for better opportunity in Data Science or Analytics domain ​
Research scholars, academicians and scientists who wish to use Data Science applications in their respective areas of work & projects.​
Corporate business professionals who wishes to learn data science applications to manage & run analytics processes​
IT enabled Services executives ,database developers,business analysts, business intelligence and analytics who wish to learn Data science implementations​
  • Extracting data from multiple sources and summarizing in the database tables for Analytics implementation. Various SQL features like Joins,unions, advanced Joins and, complex queries like - sub queries, coalesce, case statement, having etc.
  • Spreadsheet functionalities on how to input data, share files; play with ribbons and creating workbooks, format worksheets using different options, rows and column controls, auto-fill and auto sum, use formulae and mathematical functions.
  • Editing features, filtering and sorting, printing setup and artistic effect as well as advanced features such as using functions, formulae, slicers, creating pivot tables and working with advanced ‘if’ conditions while making use of tables ;charts; graphs and dashboards for understanding the use of statistical tools in Excel.
  • Create Interactive dashboard reporting critical for any business using Tableau, to carry out drag and drop calculations, analytics, reference and trend lines, geography search and lasso selection; for intuitive data visualization, decision making and publishing the analyzed data locally or on the cloud.
  • R code writing for data manipulations, statistical functions, complex calculations& graphical display of outputs including certain techniques like assign variables, analyze vectors, matrices, factors, lists ,data frames and functions to name a few
  • Develop skills in Machine learning Techniques such as - Random Forests, Decision Trees, Artificial Neural Networks, Support Vector Machines, K-Nearest Neighbor, Ensemble and Gradient Boosting.
  • Coding in Python along with exploring the various functionalities; This course will also introduce you to different Python IDE's like Spyder, Jupyter Notebook along with Python operators, conditional statements, loop concepts data structures, built in operators , functions along with libraries like Numpy,Pandas and visualizations using matplotlib
Any previous knowledge of programming skill would suffice. Familiarity with any other packaged software or spreadsheet would be of help.

Description

The course covers 5 modules viz SQL, Excel, Tableau R & Python and will equip learners with skills in data management, reporting, visualization, advanced analytics and machine learning with R & Python.
This SQL course provides a solid foundation of the SQL language that covers the concepts of SQL programming including database objects, storing, retrieving and manipulating data using various techniques. Learning SQL will allow you to mine data with greater efficiency. The training on Excel will make you more proficient with powerful reporting& analysis. While business reporting is critical for all organizations for good decision making analyzing data with basic statistical functions using Excel is an added advantage of this curriculum.
Tableau with its range of business intelligence applications is known for its flexible intuitive & interactive visualization for effective business insights. Tableau software has real-time data analytics capabilities and supports cloud as well. The benefits of using Tableau software are its flexibility to explore data with its advanced visualization feature, drill up or down across multiple dimensions of business that could provide effective measures so that monitoring & controlling could be done after carefully studying the trends & patterns.
R is the most popular programming languages used for data analysis and statistical modelling.This training course provides an in dept understanding of data analytics concepts as well as application of R language in data manipulation, various calculation and graphical display. In addition this content covers statistical functions and techniques and machine learning which will enable you to learn how to user R features such as random forest and decision trees for applications to learn from data and arrive at decisions with minimal human intervention.
Python is a general-purpose programming language which can be used for a wide spectrum of applications such as business data analysis, artificial intelligence and scientific computing. Python is built on OOPS (Object Oriented Programming) and caters to a wide spectrum of analytical needs ranging from data access, data wrangling, munging, dashboards, visualizations & a comprehensive exposure to state of the art Machine Learning and Artificial Intelligence techniques.

Who Should Attend?

Students from any discipline who are looking for better opportunity in Data Science or Analytics domain ​

Research scholars, academicians and scientists who wish to use Data Science applications in their respective areas of work & projects.​

Corporate business professionals who wishes to learn data science applications to manage & run analytics processes​

IT enabled Services executives ,database developers,business analysts, business intelligence and analytics who wish to learn Data science implementations​.

WHAT YOU WILL LEARN

  • Extracting data from multiple sources and summarizing in the database tables for Analytics implementation. Various SQL features like Joins,unions, advanced Joins and, complex queries like - sub queries, coalesce, case statement, having etc.
  • Spreadsheet functionalities on how to input data, share files; play with ribbons and creating workbooks, format worksheets using different options, rows and column controls, auto-fill and auto sum, use formulae and mathematical functions.
  • Editing features, filtering and sorting, printing setup and artistic effect as well as advanced features such as using functions, formulae, slicers, creating pivot tables and working with advanced ‘if’ conditions while making use of tables ;charts; graphs and dashboards for understanding the use of statistical tools in Excel.
  • Create Interactive dashboard reporting critical for any business using Tableau, to carry out drag and drop calculations, analytics, reference and trend lines, geography search and lasso selection; for intuitive data visualization, decision making and publishing the analyzed data locally or on the cloud.
  • R code writing for data manipulations, statistical functions, complex calculations& graphical display of outputs including certain techniques like assign variables, analyze vectors, matrices, factors, lists ,data frames and functions to name a few
  • Develop skills in Machine learning Techniques such as - Random Forests, Decision Trees, Artificial Neural Networks, Support Vector Machines, K-Nearest Neighbor, Ensemble and Gradient Boosting.
  • Coding in Python along with exploring the various functionalities; This course will also introduce you to different Python IDE's like Spyder, Jupyter Notebook along with Python operators, conditional statements, loop concepts data structures, built in operators , functions along with libraries like Numpy,Pandas and visualizations using matplotlib

PREREQUISITES

Any previous knowledge of programming skill would suffice. Familiarity with any other packaged software or spreadsheet would be of help.

Curriculum

SQL Curriculum

This section gives a high level introduction to SQL and lays the foundation for the various features of SQL.
  • Introducttion to SQL
  • Different Commands like DDL, DML, DCL
  • Data and Database Objects
  • Schema
This section covers a very important element in relational databases and flat file databases namely Tables. This section also covers about the various operations that can be performed on table
  • What is a Table
  • Update, Drop and Delete Table Using SQL
This section covers the various operations and functions available in SQL to query the data from the database.
1)Data and column selection
  • selecting a Sample
  • Selecting Top Rows
Section covers various editing   options
  • Add & Rename Column
  • Cast and Case Statement
  • SQL to Date
  • SQL Distinct
  • Where Clause
  • In Function
  • Between Function
  • Alias
  • CONCATENATE
  • TRIM
  • LENGTH
  • LIKE
This section covers the various types of joins to assemble the base data from multiple tables.
  • SQL Inner Join and Outer Join
  • Left Join and Right Join
Advance SQL Joins
  • Cross Join
  • Union and Union All
This section covers a wide variety of functions which are useful while querying data from database.
  • SQL Minus
  • Coalesce
The chapters cover some of the useful mathematical operators
  • Average, Count, subtraction, Multiplication, Division
  • Max, Min, Sum, Round
This section covers the various functions used to summarize complex data into useful business decisions.
  • Group by Clause
  • Having Clause
  • SQL null Function
  • Sub-Query
  • Convert
  • Substring
  • In string

Excel Curriculum

The following topics provides an overview of excel and introduces to the different features of excel
  • Different versions of Excel
  • Introduction to Spreadsheets & Basic Spreadsheet Skills
  • Starting to work with Excel
  • Types of data
  • Placing Cell Alignment & its functionalities
  • Excel Help System.
  • Short Cut Keys.
The below mentioned topics covers the basics of Excel and illustrates how to use a Excel Workbook.
  • Opening & Closing Workbooks.
  • Page Layout & its Functionalities.
  • Understanding Workbook File Formats.
The following topics cover formatting, file handling and other useful features of a spreadsheet.
  • Selecting Cells
  • How to Insert Row & Column
  • How to play with Sheet
  • Importance of Cell Referencing
  • Formatting Number & Cell.
  • Editing, Copying & Moving cells
  • Proofing
  • Find & Replace
  • Sum, Average & Range
  • Count & Count A functionality & difference
  • Auto Sum & Auto Fill Function
  • Add Comments
The below mentioned topics gives an overview of built in functions in excel.
  • Truncate Function
  • Features of each menu in Ribbons & Toolbars
  • Trim, Round, Transpose – Functionalities
  • Uses of Lower, Upper, Proper functions
  • Concatenation
  • Match Index
  • IS Error
  • IS Number.
The chapter introduces how to manipulate data with Excel
  • Freeze Headers
  • Filtering & Sorting.
  • Remove Duplicates & Listing Options
  • VLOOKUP’s
  • HLOOKUP’s
  • Formula Auditing and Error Tracing
  • Subtotals and Grouping.
This section gives an overview of the summarizing and reporting in Excel.
  • Pivot Tables
    • Filtering and Sorting a PivotTable
    • Changing a PivotTables Calculation
    • Updating a PivotTable
  • Pivot Slicers
This module covers visualization and graphical features in excel
  • Understanding chart layout elements
  • Adding a chart title & axes titles
  • Positioning the legend
  • Showing data labels & data table
  • Modifying the axis & Formatting the plot area
  • Drawing shapes in a chart.
This module covers very useful conditional statements in excel.
  • Hyperlinks in Excel
  • IF Conditions
  • Sum IF
  • Average IF
  • Count IF
  • Creating the AND function within an IF
  • Creating the OR function within an IF statement
  • The NOT function.

This chapter introduces to the statistical techniques that can be used with Excel like descriptive statistics, test of hypothesis & relationship between variables.

Tableau Curriculum

This section gives a high level overview of Business Intelligence and its application.
  • Dashboards,
  • Reporting
  • Visualizations
  • Data Preparation
  • Modern Data Warehousing
  • Self-Service Business Analytics
  • Big Data & Advanced Analytics
  • Planning & Forecasting Systems
This chapters covers an introduction to Tableau and its salient features.
  • About Tableau
  • Why Tableau?
  • Tableau reporting architecture
  • Tableau Products
This section covers the different data types in Tableau.
  • Measures & Dimensions
  • Continuous & Discrete data
The following topics covers various operations in Tableau used for creating dashboards.
  • Dashboards
  • Reporting
  • Visualizations
  • Filter
  • Sort
  • Colours
  • Size
  • Label
  • Detail
Advanced features for creating dashboards in Tableau.  
  • Options In Tableau
  • Worksheets and Dashboards
  • Customizing Filters
  • Filter Actions & Functionalities
  • Row Shelf & Column Shelf
  • Marks cards
  • Tooltip, Path, Sets
  • Creating Parameters & Using Parameters
  • Groups
  • Calculated Columns
The chapters broadly details on  the various types of graphs used to summarize the data in Tableau.
  • Pie Charts , Bar Charts, Stacked bar charts, Dual lines
  • Highlight tables
  • Heat maps, Symbol maps, Tree maps, Filled Maps
  • Circle views & Histogram
  • Area Charts – (Continues & Discrete)
  • Combination charts, Scatter Plots & Box Plots
  • Gantt charts, Bullet Charts, Packed bubble charts
This section discusses the various formatting and data munging tools in Tableau.
  • Building Dashboards
  • Trend Lines & Forecasting
  • Reference Bands & Lines
  • Show Missing Values & Handling Null Values
  • Legend Highlighting & Layout Containers
  • Visually Grouping Data
  • Table Calculations
  • Computing Totals
  • Formatting & Annotating
  • Tiling & floating dashboards
  • Tableau Server & Sharing Server views

Python Curriculum

This chapter provides a brief overview about Python programming language
  • History, Why Python?
In this module you will learn to install Python and set it up.
  • Environment Setup & Variables
  • Getting Python & Setting Path
You will learn the characteristics & salient features of Python
  • My first Python program
  • Identifiers
  • Reserved Words
  • Lines and Indentation
  • Command Line Arguments
In this chapter you will get introduced to a detailed study on various operations performed in Python
  • Arithmetic Operations & Number Methods
  • Accessing a value from Strings
  • Updating Strings & String Methods
  • Comparison & Assignment Operators
Understand how Lists, Tuples and Dictionaries are used in Python
  • Accessing, Updating & Deleting elements
This chapter covers the different types of loops & conditional statements
  • IF, ELSE statements & Nested IF statements
  • While, For, Break, Continue etc.

This chapter describes the various built-in functions & generators

In this chapter you will learn how data transformation is performed in Python
  • List processing & Conversions from 1 type to other

This topic covers simple & advanced regular expression to handle string data

This chapter covers various data accessing features in Python
  • Import Statement
  • Executing and Locating Modules
  • Exception Handling

This chapter provides various summary statistics like mean, median & mode with helps in data compression

In this chapter you will learn high level overview of the important libraries used in Data Science

This chapter describes step by step procedures to construct validate & test hypothesis needed for statistical analysis
  • Hypothesis Testing
  • Confidence Intervals
  • T-Test
  • Contingency Tables
  • Cross Tabs
  • Chi-Square significance test
  • Correlation
This chapter covers the different visualization techniques in Python
  • Bar
  • Line
  • Pie
  • Histogram

This chapter broadly describes various data munging & wrangling techniques using Pandas library

  • Importing and Exporting flat files
  • Operations on Pandas Data frames
  • Summary Statistics
  • Sub setting the Data frames
  • Handling missing values in the Data frames

This chapter introduces you to different Numpy libraries useful for handling quantitative data

The chapter details on high level overview on different machine learning techniques
  • Machine Learning with Supervised
  • Machine Learning with Unsupervised
  • Introduction to Deep Learning
The chapter describes various techniques used to segment data
  • Hierarchical Clustering
  • K-means
This chapter discusses the predictive modeling algorithms used to model multi-variate relationships in the data using Python
  • Simple Linear &  Multiple Linear Regression
  • Logistic Regression
The chapter explains three important algorithms to identify various profiles used in decision making
  • Decision trees
  • Random forest
  • Gradient Boosting

The chapter discusses the different techniques to model historical time series data using Python.

R Curriculum

Get an overview of OOPS concepts through R programming
  • R Environment
  • Using R Interactively
  • R commands, case sensitive etc.
Learn how to use operations applicable to simple manipulations; numbers & vectors.
  • Vectors & Assignment
  • Vector Arithmetic
  • Generating regular sequences
  • Logical vectors & Index vectors
  • Missing values
  • Character values
  • Array indexing
  • Index matrices
  • Forming partitioned matrices
  • Frequency tables from factors
  • The concatenation function with arrays
Learn how to use Lists & Data Frames
  • Constructing & modifying Lists
  • Working with data frames
  • Managing search path

Know how Open database connectivity is used in R

Reading data from files & different sources

The chapter gives an overview of introduction various theoretical distribution of data
  • Checking normality/Normal distribution
  • Frequency distributions & Contingency Tables
  • Binning
  • Binomial distributions
  • IQR & Empirical rule for symmetric distributions
  • Probability distributions
  • Proportion tables & Confidence Interval
This chapter covers various parametric tests used in statistics to validate Hypothesis
  • T-test
  • F-test
  • Sampling distributions
  • Chi-Square
  • ANOVA
  • Correlation

The below topics describes various visualization techniques to show the relationship of the variables 

  • Bar-plot
  • Pie chart
  • Stacked Bar chart
  • Histogram
  • Line chart

The below mentioned topics cover unsupervised machine learning techniques

  • Cluster Analysis
  • Principal Component Analysis
  • Factor Analysis
Learn how to use supervised machine learning algorithms Regression Analysis
  • Simple Linear Regression
  • Multiple Linear Regression
  • Logistic Regression

Learn how to build robust machine learning models when data is non linear

  • Random Forests
  • Decision Trees
  • Support Vector Machines
  • K-Nearest Neighbor
  • Ensemble
  • Gradient Boosting
  • Regularization
  •  
This topic introduces to the backbone of artificial intelligence including architecture of neural networks
  • Artificial Neural Networks
 

In this chapter learn easy to use interactive web based dashboards with R

  • R-Shiny

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Testimonials

I completed the Introduction to Business Analytics training course conducted by Kaalp Consulting. The course material was easy to understand and I got hands on experience to practice the software commands. The instructors had good knowledge and gave us practical tips on application of analytics in problem solving. I was very satisfied with the training course.
BBA Student, Bangalore
Kaalp Consulting conducted a training program in R Programming for our institute. The course material is very extensive and well designed. The instructors were working professionals who have good experience in using R in business analytics. They explained the topics extremely well and gave us a good practical understanding on how R is used in real life scenarios. We very satisfied with the course and highly recommend it to others.
Lecturer and Trainer. TN.
Kaalp Consulting conducted a course on Introduction to Business Analytics with SQL, Excel and Tableau for our B.Com and BBA students. We were very pleased with the manner in which the courses were presented by their instructors who are industry professionals. The instructors explained the topics very well and shared their practical experience on how the business analytics is applied in real life. The course content is well designed and the program was managed very professionally by Kaalp Consulting. We have no hesitation in recommending them to any institution or individual who are interested in joining their training courses.
Dr. Geethu, KJC, Bangalore

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